VISUAL NARRATIVES OF WEATHER–CROP DYNAMICS: INTELLIGENT FORECASTING AND PRECISION CROP PROTECTION USING GFLOWNET MODELS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i2s.2026.7268Keywords:
GFlowNet, Weather–Crop Interaction Modeling, Intelligent Forecasting, Precision Crop Protection, Generative Decision Modeling, Climate-Resilient Agriculture, Sequential Decision OptimizationAbstract [English]
A history of challenges in intelligent agriculture has been inaccurate modeling of weather-crops interactions, which is characterized by climatic variability, nonlinearity of crop responses, delayed interventions, and uncertainty in future weather patterns. Current methods including statistical prediction using control based on rules, independent machine learning predictors and traditional reinforcement learning have constrained myopic decision-making, ineffective uncertainty management and inefficient optimization of long-term policies. To address such shortcomings, this paper will recommend a GFlowNet-Enhanced Weather-Crop Interaction Modeling framework to intelligent forecasting and precision crop protection. The approach presents a combination of probabilistic weather prediction and Generative Flow Networks (GFlowNets) to explicitly learn several viable weather-action-crop evolution procedures to allow sampling of high-reward decision trajectories, as opposed to making use of an optimal forecast or policy. Such a formulation of decision in the generation is paired with sequential planning of crop protection to bring about the uncertainty of the forecast to the adaptive and resource-conscious interventions. The proposed model attains a higher forecast accuracy of 97.54 as compared to the baseline methods with a considerable improvement in crop loss reduction, resource utilization and reliability in response to extreme events. It is assessed on five dimensions and they vary: predictive accuracy, reducing losses of crops, effectiveness in using resources, flexibility in extreme weather conditions, and stability of decisions in long-term. Agro-climatic and crop management dataset evaluation Benchmark testing shows that agro-climatic and crop management strategies have steadily better performance than machine learning and reinforcement learning based baselines, such as reduced intervention costs, resilience to uncertainty, and seasonal policy generalization. The discussion in this paper will cover mathematics formulation, algorithmic architecture using GFlowNet, the comparison of the algorithm with other methods, and implications of the algorithm to a scalable implementation. The findings prove the generative decision modeling in a GFlowNet-driven approach to be a promising research agenda in building climate-resilient predictions and next-generation precision crop protection technology.
References
Arumugam, P., Karuppasamy, K., and Subramanian, S. (2021). Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using MODIS LAI and Gradient Boosted Regression in India. Remote Sensing, 13(12), 2379. https://doi.org/10.3390/rs13122379 DOI: https://doi.org/10.3390/rs13122379
Baio, F. H. R., Santana, D. C., Teodoro, L. P. R., de Oliveira, I. C., Gava, R., de Oliveira, J. L. G., Junior, C. A. d. S., Teodoro, P. E., and Shiratsuchi, L. S. (2023). Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management. Remote Sensing, 15(1), 79. https://doi.org/10.3390/rs15010079 DOI: https://doi.org/10.3390/rs15010079
Botero-Valencia, J., García-Pineda, V., Valencia-Arias, A., Valencia, J., Reyes-Vera, E., Mejia-Herrera, M., and Hernández-García, R. (2025). Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives. Agriculture, 15(4), 377. https://doi.org/10.3390/agriculture15040377 DOI: https://doi.org/10.3390/agriculture15040377
Braidotti, G., De Nobili, M., and Piani, L. (2020). Integrated Use of Local and Technical Soil Quality Indicators and Participatory Techniques to Select Them: A Review of Bibliography and Analysis of Research Strategies and Outcomes. Sustainability, 13(1), 87. https://doi.org/10.3390/su13010087 DOI: https://doi.org/10.3390/su13010087
Elbasi, E., Zaki, C., Topcu, A. E., Abdelbaki, W., Zreikat, A. I., Cina, E., Shdefat, A., and Saker, L. (2023). Crop Prediction Model Using Machine Learning Algorithms. Applied Sciences, 13(16), 9288. https://doi.org/10.3390/app13169288 DOI: https://doi.org/10.3390/app13169288
Hernández Hernández, G. C., Gómez Gómez, J., and Jiménez-Cabas, J. (2025). Predictive Models Based on Artificial Intelligence to Estimate Crop Yield: A Literature Review. Agriculture, 15(23), 2438. https://doi.org/10.3390/agriculture15232438 DOI: https://doi.org/10.3390/agriculture15232438
Iqbal, N., Shahzad, M. U., Sherif, E.-S. M., Tariq, M. U., Rashid, J., Le, T.-V., and Ghani, A. (2024). Analysis of Wheat-Yield Prediction Using Machine Learning Models Under Climate Change Scenarios. Sustainability, 16(16), 6976. https://doi.org/10.3390/su16166976 DOI: https://doi.org/10.3390/su16166976
Ji, Z., Pan, Y., Zhu, X., Zhang, D., and Dai, J. (2022). Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective. Agriculture, 12(8), 1263. https://doi.org/10.3390/agriculture12081263 DOI: https://doi.org/10.3390/agriculture12081263
Kong, J.-L., Fan, X.-M., Jin, X.-B., Su, T.-L., Bai, Y.-T., Ma, H.-J., and Zuo, M. (2023). BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture. Agronomy, 13(3), 625. https://doi.org/10.3390/agronomy13030625 DOI: https://doi.org/10.3390/agronomy13030625
Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., and Uwamahoro, A. (2023). Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize. Agriculture, 13(1), 225. https://doi.org/10.3390/agriculture13010225 DOI: https://doi.org/10.3390/agriculture13010225
Li, L., Wang, B., Feng, P., Liu, D. L., He, Q., Zhang, Y., Wang, Y., Li, S., Lu, X., and Yue, C., et al. (2022). Developing Machine Learning Models with Multi-Source Environmental Data to Predict Wheat Yield in China. Computers and Electronics in Agriculture, 194, 106790. https://doi.org/10.1016/j.compag.2022.106790 DOI: https://doi.org/10.1016/j.compag.2022.106790
Li, Z., Nie, Z., and Li, G. (2024). Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield. Agronomy, 14(4), 777. https://doi.org/10.3390/agronomy14040777 DOI: https://doi.org/10.3390/agronomy14040777
Liu, Z., Ju, H., Ma, Q., Sun, C., Lv, Y., Liu, K., Wu, T., and Cheng, M. (2024). Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China. Agriculture, 14(4), 638. https://doi.org/10.3390/agriculture14040638 DOI: https://doi.org/10.3390/agriculture14040638
Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., Borodulin, A., and Tynchenko, Y. (2024). Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools. Sustainability, 16(21), 9437. https://doi.org/10.3390/su16219437 DOI: https://doi.org/10.3390/su16219437
Mancini, A., Solfanelli, F., Coviello, L., Martini, F. M., Mandolesi, S., and Zanoli, R. (2024). Time Series from Sentinel-2 for Organic Durum Wheat Yield Prediction Using Functional Data Analysis and Deep Learning. Agronomy, 14(1), 109. https://doi.org/10.3390/agronomy14010109 DOI: https://doi.org/10.3390/agronomy14010109
Manogaran, G., Hsu, C. H., Rawal, B. S., Muthu, B., Mavromoustakis, C. X., and Mastorakis, G. (2022). ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0. IEEE Internet of Things Journal, 8, 3120–3129. https://doi.org/10.1109/JIOT.2020.3045479 DOI: https://doi.org/10.1109/JIOT.2020.3045479
Mouratiadou, I., Lemke, N., Chen, C., Wartenberg, A., Bloch, R., Donat, M., Gaiser, T., Basavegowda, D. H., Helming, K., and Yekani, S. A. H., et al. (2023). The Digital Agricultural Knowledge and Information System (DAKIS): Employing Digitalisation to Encourage Diversified and Multifunctional Agricultural Systems. Environmental Science and Ecotechnology, 16, 100274. https://doi.org/10.1016/j.ese.2023.100274 DOI: https://doi.org/10.1016/j.ese.2023.100274
Moysiadis, V., Tsakos, K., Sarigiannidis, P., Petrakis, E. G. M., Boursianis, A. D., and Goudos, S. K. (2022). A Cloud Computing Web-Based Application for Smart Farming Based on Microservices Architecture. In Proceedings of the 11th International Conference on Modern Circuits and Systems Technologies (MOCAST) 1–5). https://doi.org/10.1109/MOCAST54814.2022.9837727 DOI: https://doi.org/10.1109/MOCAST54814.2022.9837727
Nyéki, A., and Neményi, M. (2022). Crop yield prediction in precision agriculture. Agronomy, 12(10), 2460. https://doi.org/10.3390/agronomy12102460 DOI: https://doi.org/10.3390/agronomy12102460
Shaikh, T. A., Mir, W. A., Rasool, T., and Sofi, S. (2022). Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk. Archives of Computational Methods in Engineering, 29, 4557–4597. https://doi.org/10.1007/s11831-022-09761-4 DOI: https://doi.org/10.1007/s11831-022-09761-4
Singh, K., Yadav, M., Barak, D., Bansal, S., and Moreira, F. (2025). Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting. Sustainability, 17(10), 4711. https://doi.org/10.3390/su17104711 DOI: https://doi.org/10.3390/su17104711
Sishodia, R. P., Ray, R. L., and Singh, S. K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136 DOI: https://doi.org/10.3390/rs12193136
Teodoro, P. E., Teodoro, L. P. R., Baio, F. H. R., Junior, C. A. d. S., dos Santos, R. G., Ramos, A. P. M., Pinheiro, M. M. F., Osco, L. P., Gonçalves, W. N., and Carneiro, A. M., et al. (2021). Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data. Remote Sensing, 13(22), 4632. https://doi.org/10.3390/rs13224632 DOI: https://doi.org/10.3390/rs13224632
Thimmegowda, M. N., Manjunatha, M. H., Huggi, L., Shivaramu, H. S., Soumya, D. V., Nagesha, L., and Padmashri, H. S. (2023). Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka. Agronomy, 13(3), 704. https://doi.org/10.3390/agronomy13030704 DOI: https://doi.org/10.3390/agronomy13030704
van Klompenburg, T., Kassahun, A., and Catal, C. (2020). Crop Yield Prediction Using Machine Learning: A Systematic Literature Review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709 DOI: https://doi.org/10.1016/j.compag.2020.105709
Xu, W., Kaili, Z., and Tianlei, W. (2021). Smart Farm Based on Six-Domain Model. In Proceedings of the IEEE 4th International Conference on Electronics Technology (ICET) (417–421). https://doi.org/10.1109/ICET51757.2021.9451003 DOI: https://doi.org/10.1109/ICET51757.2021.9451003
Yasam, S., Nair, S. A. H., and Kumar, K. S. (2022). Supervised Learning-Based Seed Germination Ability Prediction for Precision Farming. Soft Computing, 26, 13133–13144. https://doi.org/10.1007/s00500-022-06910-6 DOI: https://doi.org/10.1007/s00500-022-06910-6
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Copyright (c) 2026 Harikrishna B. Jethva, Dr. Vivekanandam, Dr. Eugenio Vocaturo, Mehulkumar J. Vasava
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